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Jiuyong Li

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Conference Open access Sep 2026

PhyTTA: Physics-Informed Test-Time Adaptation of Foundation Models for Regional Drought Prediction

Drought prediction is crucial for disaster mitigation, yet it remains challenging because regional droughts reflect nonlinear interactions among precipitation supply, evaporative demand, and slowly varying land atmosphere states. Although time series foundation models (TSFMs) have shown strong zero shot performance in...

Wen-Tao Gao, Jiuyong Li, Lin Liu et al. · 0 citations
#machine learning Book Open access Jul 2026

Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting

A lightweight, black-box adaptation framework that enhances frozen TSFMs at inference time through two plug-and-play wrappers that consistently improves forecasting performance across several backbone models, demonstrating up to 26% mean squared error reduction over the corresponding frozen backbone while enabling prac...

Wen-Tao Gao, Jiu-Yong Li, Lin Liu et al. · 1 citation
#software testing Open access Sep 2026

A novel causality-based method for identifying drivers of breast cancer progression

Abstract Motivation Identifying transcriptomic factors with potential causal effects on breast cancer progression is important for understanding disease mechanisms and prioritizing therapeutic targets. However, causal-effect estimation from high-dimensional gene-expression data remains challenging because of the large...

Lai-Ping Shen, Ying-Hao Zhang, Xiao-Yan Zhou et al. · 0 citations
Preprint Aug 2026

Transportable Causal Effect Estimation across Networks under Interference

TranCE (Transported Causal Effects), a doubly-robust algorithm combining an interventional outcome model, a domain density-ratio correction, and cross-fitted inference, is presented, which has the potential to improve intervention strategies in networked systems, particularly in social networks and public health.

Xiaojing Du, Jiuyong Li, Lin Liu et al. · 0 citations
Review Open access Jul 2026

Explainable Hallucination Mitigation in Large Language Models: A Survey

This survey addresses hallucination mitigation through the lens of explainability, proposing a taxonomy that distinguishes between internal explainability and post hoc explainability and discusses the constructive role of hallucinations in creative and user‐driven applications.

Wentao Deng, Jiao Li, Hongyu Zhang et al. · 1 citation

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